Related Experiment Video
Updated: Jun 20, 2026

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei
Published on: February 17, 2014
Mathematical modeling and spatial evolutionary analysis of tuberculosis transmission across diverse demographic
Ilham Saiful Fauzi1, Nuning Nuraini2, Arrofiatuz Zahra3
1Department of Accounting, Politeknik Negeri Malang, Malang, Indonesia.
None:
The resurgence of TB cases following the COVID-19 pandemic has raised concerns about previously masked transmission driven by undetected and untreated infections resulting from disruptions in TB health services. Understanding post-pandemic transmission dynamics and identifying high-risk areas are therefore critical for effective TB control, particularly in high-burden settings. This study employed an ecological time-series design using TB notification data recorded in the national reporting system from January 2020 to October 2024 across all districts. A compartmental mathematical model incorporating vaccination, testing rates, detected and undetected infections, and multidrug-resistant TB was developed to characterize transmission dynamics and assess intervention impacts. Model simulations showed strong agreement with observed data, as indicated by a Pearson correlation of r=0.825. The estimated basic reproduction number was R0=3.999 (95% CI: 3.679 - 4.319), substantially exceeding the epidemic threshold and indicating sustained transmission potential. Sensitivity analysis identified the testing rate as a key determinant of transmission, accounting for a 19.9% reduction in R0, with numerical simulations demonstrating that increased testing significantly reduces undetected cases and TB-related mortality. Spatial analyses revealed a higher transmission risk in eight urban or suburban areas in the central and western regions, alongside persistent coldspot clusters in predominantly rural southeastern areas. Notably, despite higher incidence and infection rates, urban areas exhibited lower R0 values, likely reflecting more effective vaccination impact and better healthcare access. These findings provide empirical evidence supporting strengthened testing strategies, optimized vaccinations in high-density urban settings, and improved healthcare access in rural areas to reduce post-pandemic TB transmission.
Related Concept Videos
Statistical Methods for Analyzing Epidemiological Data
Pulmonary Tuberculosis II
Here is a detailed explanation of its pathophysiology:
Transmission: The process begins when a person inhales droplet nuclei containing M. tuberculosis. These are typically released into the air when an individual with pulmonary or...
Causality in Epidemiology
Pulmonary Tuberculosis I
Causative Organism
The primary infectious agent causing tuberculosis is Mycobacterium tuberculosis, a slow-growing, acid-fast, aerobic rod that exhibits sensitivity to heat and ultraviolet light. Instances of Mycobacterium bovis and Mycobacterium avium contributing to the development of TB infection are rare.
Mode of...
Pulmonary Tuberculosis III
The first classification is based on the development of the disease, and it includes the following categories:
Population Growth

